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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 1651–1675 of 1773 papers

TitleStatusHype
Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary—0
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation—0
Sequence-to-Dependency Neural Machine Translation—0
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation—0
Visualizing and Understanding Neural Machine Translation—0
Chunk-based Decoder for Neural Machine Translation—0
Stronger Baselines for Trustable Results in Neural Machine TranslationCode0
Memory-augmented Chinese-Uyghur Neural Machine Translation—0
English-Japanese Neural Machine Translation with Encoder-Decoder-ReconstructorCode0
Encoder-Decoder Shift-Reduce Syntactic ParsingCode0
THUMT: An Open Source Toolkit for Neural Machine TranslationCode0
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation—0
Towards Neural Phrase-based Machine TranslationCode0
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality EstimationCode0
Attention-based Vocabulary Selection for NMT Decoding—0
Search Engine Guided Non-Parametric Neural Machine Translation—0
Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPUCode0
Chunk-Based Bi-Scale Decoder for Neural Machine TranslationCode0
Modeling Source Syntax for Neural Machine Translation—0
A Teacher-Student Framework for Zero-Resource Neural Machine Translation—0
Deep Neural Machine Translation with Linear Associative Unit—0
A GRU-Gated Attention Model for Neural Machine Translation—0
Neural System Combination for Machine Translation—0
Adversarial Neural Machine Translation—0
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification—0
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